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  • Leneve 12:00 am on December 19, 2011 Permalink | Reply  

    Introduction to Distribution Planning Costing 

    I think we often take for granted the work that occurs on the ground when it comes to electricity service. Ever wonder who pays for the crew to come out to build new lines, hook-up new connections, or fidget with the substations? We aren’t sent itemized bills with the cost of trimming the two trees that blocked the way or the long line that had to be pulled from the street to get to the house with the large setback. How does this massive infrastructure get funded despite all the situational complexity?

    It is no surprise that marginal T&D capacity costs are area-specific and vary greatly. A costing methodology study by Energy & Environmental Economics, Inc. and Pacific Energy Associates looked at four utilities and obtained estimates from $73 to $556 per kW, and these are just averages of much larger ranges for each utility. An important reason is because costs are driven by peak loads in areas that need congestion relief. In other words, T&D costs are essentially price-driven since they translate into the rates charged for energy that generate income.

    Due to the peaky nature of loads, utilities must be sensitive to location, time of year, and time of day when evaluating planning decisions. Demand response is ideally suited as a potential solution, given its similar dependencies. The difficult task is finding the proper match between demand and potential resource that will provide net benefits for the utility, resource owner, and any other involved parties. On the utility side, current practice for T&D planning generally involves (1) identifying problem areas, (2) developing and evaluating potential solutions, and (3) allocating budget for the best projects. For a demand response (DR) program vs. a traditional technical solution (e.g. capital budgeting for a new line), understanding and possibly modifying the methodology for evaluating alternatives should be considered.

    There are generally five cost tests that can be applied to utility projects. The Utility Cost Test (UCT) differs from the Rate Impact Measure (RIM) in that it measures as costs all the expenses that affect the ratebase, rather than impact the rate itself. The rate is the ratebase divided by sales, thus projects that impact sales will create different results between the two tests. The Total Resource Cost (TRC) considers the cost to the utility and customer as a whole, so transfers of cost between them aren’t recognized. The Societal Cost Test (SCT) extends the TRC to consider externalities. Of course, the participant alone can be considered, who would see incentives, revenue, and net costs of whatever program being subscribed to.

    The elements that go into cost evaluations can be difficult to quantify. Environmental effects are usually monetized into cost per pound of air emissions or per kWh of energy. Power quality is translated into outage costs, repair costs, and/or value if quality-differentiated rates exist. Power reliability are usually treated as constraints, so meeting reliability index criteria become important. Even more difficult to quantify is managing risk (i.e. low freq and high impact events, probability estimation, cost estimation) and option/strategic value, which is from the flexibility to respond to by situation with limited information. Advanced methods that can be used include dynamic programming, game theory, contingent claims analysis, financial derivatives, and decision analysis.

    The study also lists these as major cost drivers: location, load growth, load shape, equipment characteristics, operational details, financial parameters, synergies, environmental considerations, PQR, uncertainty, and intangibles (e.g. public relations, learning experience). They are not mutually exclusive of one another and can share some dependencies.

    In terms of ranking and selecting projects, criteria and feasibility constraints must be met. Criteria refers to the usual financial measures of PV of cost, NPV, levelized cost, IRR, payback time, and benefit-cost ratio, as well as engineering standards, incremental measures of those technical aspects, and utility function. Feasibility refers to constraints placed by technology, budget, regulation, social and political, and the participants. Evaluating these with respect to an individual project, simple portfolio, interdependent portfolio, or even better, dynamically programmed, provide valuable decision support. Still, senior management decision-making based on simply experience and judgement is common.

    Costs should be allocated by location and time, and methods exist for primarily measuring area- and time-specific marginal costs (ATSMC). Area-specific analysis is applicable for expansion plans, where budget is allocated based on engineering-defined boundaries, if possible. If not, zones are used to differentiate approximate costs. Facility sharing can be dealt with based on how load is allocated, for which there are several indices used in literature, but requires hourly load data and is thus historical and not necessarily predictive. As for time dependency, attributing costs to “peak block” shares and applying allocation factors is used. A peak period or block can be the top XX or all number of hours above a threshold. Allocation factors are more advanced and divide costs into each hour of the year. Two key examples are the loss-of-load-probability and peak capacity above a threshold level.

    Finally, the study recognizes that costing is not able to address some related issues if the aim is to to incorporate DR. For example, some DR alternatives need longer lead-times to implement. Reducing the review process of evaluating programs thus would enable more alternatives. Load forecasting affects some aspects of costing, but due to different methods there can be biases that planners may need to be aware of. Also, the growing awareness of DG has resulted in creative proposals from customers and customer-utility partnerships that offer risks and benefits that may not be easy to incorporate into the costing. Insuring if DR alternatives are actually clean is another issue. For public policy, the goal should be to incentivize DR by internalizing differences between stakeholders so that costing can effect socially desirable outcomes. Costing in itself is subject to public policy and thus only evaluates utility projects on existing guidelines.

    The common costing framework and methodology of where costs are derived, allocated, and evaluated is an interesting process that varies from utility to utility. Depending on existing practices, there are many aspects where improvements can be made, or challenges to be found with the expanding array of alternatives for distribution planning.

    [1] The Energy Foundation. Prepared by Energy & Environmental Economics, Inc. and Pacific Energy Associates. “Costing Methodology for Electric Distribution System Planning.” Nov. 9 2000.

    [2] Chernick, Paul and Patrick Mehr. “Electricity Distribution Costs: Comparisons of Urban and Suburban Areas.” Lexington Electric Utility Committee. Oct. 28 2003.

    [3] Filippini, Massimo and Jörg Wild. “Regional Differences in Electricity Distribution Costs and their Consequences for Yardstick Regulation of Access Prices.” May 2000.

  • Leneve 12:00 am on December 1, 2011 Permalink | Reply  

    The Value of Residential Power Quality 

      For industrial and larger commercial customers, it is easier to measure why power quality is important. Machine downtime directly affects productivity, increased maintenance and replacement impacts the bottom line, and small inefficiencies multiply into huge wastes of energy at the meter. One of the selling points of demand response is improved power quality, which benefits both the customer and the utility. LaCommare and Eto from LBNL estimated the cost of power quality disturbances was $79 billion in 2004, with 67% due to momentary interruptions and 33% due to sustained interruptions. Of the $79 billion, nearly three-quarters was attributed to commercial customers and a quarter to industrial customers. This leaves very little to the residential sector, whose costs made up 2% of the total, or $2 billion. Nearly a decade of grid R&D has since passed, but has the impact of power quality on the residential customer changed significantly? With a greater focus on household consumption, companies and utilities are looking for ways to increase acceptance of new technologies and programs. Understanding the value of power quality may provide impetus to customer adoption, or highlight continuing challenges.
      The LBNL report recognizes the difficulty of quantifying residential costs and does its best to attribute costs to not just physical goods and appliances but to the experience of a disruption in service. The report states that: “…the other “costs” borne by residential customers are experiential in nature, such as resetting clocks, changing plans, and coping with inconvenience, fear, anxiety, etc. Analytical techniques to estimate these costs typically involve contingent valuation, which includes so-called “willingness to pay” and “willingness to accept” approaches as a means of addressing experiential costs in deriving outage costs for residential customers. The findings developed through application of contingent valuation methods have been controversial due to concerns regarding bias in the responses provided by customers to the hypothetical nature of situations they must rely on.”
      The report also attempted to integrate multiple utility studies using a Tobit regression model to form cost of interruption functions, known as customer damage functions. These functions represent outage costs based on “outage duration, season, time of day, annual electricity use, and depending on the customer class, household income or number of employees.” Surveys can ask for a customer’s willingness-to-pay to avoid a certain outage scenario, rank scenarios/payment options, or estimate costs for an itemized list of mitigating actions. Other attempts to quantify residential costs generally involve surveys, but can be plagued by non-response and a lack of knowledge of the electric system for customers to provide adequate responses.
      It is recognized that outage and power quality costs are non-homogenous. The distribution of these costs across different customer categories, times, service interruption types, and other characteristics is still an active field of study. Another LBNL study uses a newer set of utility survey studies to create a two-step model based on GLM rather than loglinear regression. For comparison, they demonstrate that the earlier Tobit model underestimates costs dramatically and that a Heckman two-step model underestimates C&I costs and overestimates residential costs. Nonetheless, the lack of consistent and relevant data limits the conclusions such mega-studies can draw.
      The issue of contention focuses on how to treat the multitude of zero valued responses. Regardless of the model, the data continues to show residential customers often do not place a cost or WTP for many categories of reductions in power quality or service. This acts to reduce the economic justification for improving reliability standards. A 2011 customer satisfaction survey showed that residential electric utility customers were more satisfied in the categories examined except power quality, reliability and price. These declined by less than 10 points in a 1000 point scale. At what point does satisfaction levels translate into a real cost? On the flip side, the findings prove that there is a buffer in case disruption events occur more frequently under some circumstances. Thus, from an economic point of view there is a non-technological resiliency in the system that already exists. It would be interesting to see if this could be leveraged for promoting grid development.


    [1] LaCommare, Kristina Hamachi and Joseph H. Eto, ”Understanding the Cost of Power Interruptions to U.S. Electricity Consumers,” Ernest Orlando Lawrence Berkeley National Laboratory, Sep 2004.

    [2] Scarpa, Ricardo and Anna Alberini, Applications of simulation methods in environmental and resource economics, 2005.

    [3] Sullivan, Michael J., Ph.D., Matthew Mercurio, Ph.D., Josh Schellenberg, M.A, Freeman, Sullivan & Co. “Estimated Value of Service Reliability for Electric Utility Customers in the United States,” Jun 2009.

    [4] J.D. Power and Associates, “2011 Electric Utility Residential Customer Satisfaction Study,” Press Release, 13 July 2011. http://www.jdpower.com/news/pressRelease.aspx?ID=2011101

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